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Issue No.04 - April (2009 vol.31)
pp: 649-660
Hui Ji , National University of Singapore, Singapore
Cornelia Fermüller , University of Maryland at College Park, College Park
ABSTRACT
We present an analysis and algorithm for the problem of super-resolution imaging, that is the reconstruction of HR (high-resolution) images from a sequence of LR (low-resolution) images. Super-resolution reconstruction entails solutions to two problems. One is the alignment of image frames. The other is the reconstruction of a HR image from multiple aligned LR images. Both are important for the performance of super-resolution imaging. Image alignment is addressed with a new batch algorithm, which simultaneously estimates the homographies between multiple image frames by enforcing the surface normal vectors to be the same. This approach can handle longer video sequences quite well. Reconstruction is addressed with a wavelet-based iterative reconstruction algorithm with an efficient de-noising scheme. The technique is based on a new analysis of video formation. At a high level our method could be described as a better-conditioned iterative back projection scheme with an efficient regularization criteria in each iteration step. Experiments with both simulated and real data demonstrate that our approach has better performance than existing super-resolution methods. It can remove even large amounts of mixed noise without creating artifacts.
INDEX TERMS
Image processing software, Enhancement
CITATION
Hui Ji, Cornelia Fermüller, "Robust Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 4, pp. 649-660, April 2009, doi:10.1109/TPAMI.2008.103
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